How organisms adapt to changes in the environment is not only a central question of evolutionary biology but also relevant to the threat of recent global warming. Evolution experiments in controlled laboratory settings (Experimental Evolution) are a great tool for evaluating evolutionary processes. When combined with genome sequencing (Evolve and Resequence), genomic changes related to adaptation can be identified. Although these genomic changes can occur in large parts of a chromosome (selected haplotype block), most approaches focus only on single genomic sites, and in consequence might overestimate the signal of evolution. Here, we present a novel method for detecting such selected haplotype blocks in evolve and resequence experiments. Our approach requires only few input parameters and is based on the grouping of neighboring genomic sites and on a comparison of different chromosomes. Analyzing computer simulations and experimental data, we describe distinct haplotype block patterns related to the number of genomic sites under selection and to the speed of adaptation. Our results indicate that the analysis of selected haplotype blocks has indeed the potential to deepen our understanding of adaptation.

Read the full text here.

References

Otte KA, Schlötterer C. Detecting selected haplotype blocks in evolve and resequence experiments. Mol Ecol Resour. 2021;21:93–109. https://doi.org/10.1111/1755-0998.13244

Mallard, François, et al. A simple genetic basis of adaptation to a novel thermal environment results in complex metabolic rewiring in Drosophila. Genome biology 2018:19.1: 1-15. https://doi.org/10.1186/s13059-018-1503-4.

We aimed to sequence and compare all the DNA (eg., the genome) of a bunch of different deer mice (genus Peromyscus) species to understand how some deer mice survive in hot deserts with little to no water. A number of deer mice tissue samples were available through natural history museums, which house the raw materials for genetic and biodiversity investigations, but the samples had been collected many years earlier. Older samples produce lower quality DNA that has been broken into many pieces over time. Our normal sequencing procedure selectively removes small fragments of DNA, which would essentially throw away all the DNA we wanted to sequence for these older samples! To circumvent this, we were able to use a different DNA library preparation method called linked-read sequencing (LRS). LRS uses standard short-read sequencing technology, but adds additional information about the location of DNA fragments within the genome by bundling and barcoding DNA fragments that are located near each other prior to sequencing (eg., ‘links’ DNA fragments together in ‘genome-space’). We found that this method improves the overall quality and completeness of genome assemblies from historical tissue samples, in less time and with less effort than traditional shot-gun sequencing methods. This alternative method may be particularly valuable for building high-quality genome assemblies for extinct species for which there are no new samples being collected for or endangered species that are difficult to sample or collect. LRS adds to the suite of genomic methods that continue to unlock the secrets of natural history collections and enable fine-scale genetic measurement of change through time.

This summary was written by the study’s first author, Jocelyn Colella.

Read the full text here.

Video credit: Jocelyn Colella. Peromyscus in the field.

Full Text: Colella JP, Tigano A, MacManes MD. A linked-read approach to museomics: Higher quality de novo genome assemblies from degraded tissues. Mol Ecol Resour. 2020;20:871–881.

Biodiversity inventories can now be built by collecting and sequencing DNA from the environment, which is not only easier, faster and cheaper than direct observation, but also much more comprehensive and systematic. This gives in particular unprecedented access to little-known microbial diversity. Tapping these data to answer community ecology questions, however, can prove a daunting task, as classical statistical approaches often fall short of the size and complexity of molecular datasets. To uncover the spatial structure of soil biodiversity over 12 ha of primary tropical forest in French Guiana, we borrowed a probabilistic model from text analysis. After demonstrating the performance of the method on simulated data, we used it to capture the co-occurrence and covariance patterns of more than 25,000 taxa of bacteria, protists, fungi and metazoans across 1,131 soil samples, collected every 10 m – a dataset that led to a previous publication in Mol. Ecol. (Zinger et al. 2019). We find that, even though the forest plot is at first sight rather uniform, bacteria, protists and fungi are all clearly structured into three assemblages matching the environmental heterogeneity of the plot, whereas metazoans are unstructured at that scale. We then work though the practical problems ecologists may encounter using this approach, such as whether to use presence-absence or read-count data, how to choose the number of assemblages and how to assess the robustness of the results. Finally, we discuss the potential use of related methods in community ecology and biogeography, and argue that probabilistic models are a way forward for analyzing the ever-expanding amount of data generated by the field.

Full article:
Sommeria-Klein G, Zinger L, Coissac E, et al. (2020). Latent Dirichlet Allocation reveals spatial and taxonomic structure in a DNA-based census of soil biodiversity from a tropical forest. Molecular Ecology Resour. 20:371–386. https://doi.org/10.1111/1755-0998.13109

References:
Zinger, L., Taberlet, P., et al. (2019). Body size determines soil community assembly
in a tropical forest. Molecular Ecology, 28(3), 528–543. https://doi.org/10.1111/mec.14919